I recently quit a gig and spent a few months looking for a data science/machine learning gig. I was surprised just how gatekeeped these positions were. Everyone wanted a PhD or a masters degree. I have 15 years of experience in software. I’ve done everything to low level game programming and graphics programming to web development, to AI (but not as a specific position title). The gatekeeping in this field surprised…
My training heavily stressed bias and confounding, study design, problem specification, validation, and interpretation of results. I've seen a lot of software engineers dabbling in machine learning jump to training a deep learning model for a problem where regex would suffice. I've also seen multiple people build models that reflect the data collection instead of the biologic/medical process, and present it without even realizing how wrong their results are. The problems in these cases is often their "objective" measures of performance (e.g., precision, recall, accuracy, AUC, whatever) look pretty good, but they don't see that it's because the model AND data are both heavily influenced by this larger problem. For example, is an increase in complexity of a particular disease due to people actually being sicker or because some payer changed a reimbursement program so now billing departments are using higher acuity diagnosis codes for their patients?
That said, the best engineer I've ever met didn't have a college education. I know a bunch of awesome data scientists who have taken pretty circuitous journeys to their current career. So, "PhD required" seems like it'll lead to a lot of false negatives. So, acknowledging that, my main point is that doing data science in a meaningful and ethical way—particularly when it involves human subjects—requires a lot more thought than just being able to implement some machine learning algorithm.